Flexible manufacturing system selection using a combinatorial mathematics-based decision-making method

Rao, R. Venkata; Parnichkun, Manukid

doi:10.1080/00207540802389227

Cited by 54 publications

(29 citation statements)

References 37 publications

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“…The same FMS selection problem is now solved employing the six preference ranking methods and their relative ranking performance is shown in Table 23. From this table, it is found that there exists a perfect match between the rank ordering as obtained by ORESTE method and that derived by Rao and Parnichkun (2009). The Kendal's coefficient of concordance value is also estimated to be quite high (z = 0.9553) which indicates the existence of a near to perfect agreement between the considered methods.…”

Section: Resultsmentioning

confidence: 77%

“…A very high z value (z = 0.9778) assures the applicability of these preference ranking methods to solve this FMS selection problem too. From Tables 23 and 24, it becomes quite clear that ORESTE method provides the exact rank orderings of FMS alternatives when compared to those obtained by Rao and Parnichkun (2009). It can be concluded that ORESTE method outperforms the other methods with respect to their ranking performance.…”

Section: Resultsmentioning

confidence: 87%

“…The first problem (Rao and Parnichkun, 2009) considers selection of an FMS from a set of eight alternatives which are evaluated based on seven criteria, i.e. reduction in labour cost (%), reduction in WIP (%), reduction in set up cost (%), increase in market response, increase in quality, capital and maintenance cost ($000), and floor space used (sq.…”

Section: Resultsmentioning

confidence: 99%

“…High competition, technological advancements and continuous change in customers' demands have made the manufacturing organizations realize the importance of manufacturing flexibility, which can only be achieved through the adoption and augmentation of flexible manufacturing systems (FMSs) The past researchers have already successfully applied different mathematical techniques, like analytic hierarchy process (Wabalackis, 1988;Chan et al, 2000;Bayazit, 2005), digraph and matrix approach (Rao, 2006;Rao and Parnichkun, 2009), compromise ranking method (Rao, 2009), artificial neural network (Bhattacharya et al, 2007), data envelopment analysis (Shang & Sueyoshi, 1995;Sarkis, 1997;Talluri et al, 2000;Karsak, 2008;Liu, 2008), technique for order preference by similarity to ideal solution (TOPSIS) (Karsak, 2002;Rao, 2008), Euclidean distance-based integrated approach (Rao & Singh, 2011), axiomatic design method (Kulak & Kahraman, 2005), multi-objective programming method (Lotfi, 1995;Karsak & Kuzgunkaya, 2002), fuzzy decision algorithm (Karsak, 2002;Karsak & Tolga, 2001;Mehrabad & Anvari, 2010), multi-attribute value function (Borenstein, 1998) etc. to solve the FMS selection problems.…”

Section: Introductionmentioning

confidence: 99%

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Flexible manufacturing system selection using preference ranking methods : A comparative study

Chatterjee¹,

Chakraborty²

2014

10.5267/j.ijiec

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Flexible manufacturing systems (FMSs) offer opportunities for the manufacturers to improve their technology, competitiveness and profitability through a highly efficient and focused approach to manufacturing effectiveness. Justification, evaluation and selection of FMSs have now been receiving significant attention in the manufacturing environment. Evaluating alternative FMSs in the presence of multiple conflicting criteria and performance measures is often a difficult task for the decision maker. Preference ranking tools are special types of multicriteria decision-making methods in which the decision maker's preferences on criteria are aggregated together to arrive at the final evaluation and selection of the alternatives. This paper deals with the application of six most potential preference ranking methods for selecting the best FMS for a given manufacturing organization. It is observed that although the performances of these six methods are almost similar, ORESTE (Organization, Rangement Et Synthese De Donnes Relationnelles) method slightly outperforms the others. These methods use some preference function or utility value or Besson ranking of criteria and alternatives, to indicate how much an alternative is preferred to the others. Most of these methods need quantification of criteria weights or different preference parameters, but ORESTE method, being an ordinal outranking approach, only requires ordinal data and attribute rankings according to their importance. Therefore, it is particularly applicable to those situations where the decision maker is unable to provide crisp evaluation data and attribute weights.

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Section: Resultsmentioning

confidence: 77%

Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Flexible manufacturing system selection using preference ranking methods : A comparative study

Chatterjee¹,

Chakraborty²

2014

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…One may refer to Rao (2007) and Rao and Parnichkun (2009) for more details about how this scale is prepared.…”

Section: Fig 1 Linguistic Terms To Fuzzy Numbers Conversionmentioning

confidence: 99%

Weighted Euclidean distance based approach as a multiple attribute decision making method for plant or facility layout design selection

Rao¹,

Singh²

2012

ijiec

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In response to increasing inflexible customer demands and to improve the competitive advantage, industrial organizations have to adopt strategies to achieve cost reduction, continual quality improvement, increased customer service and on-time delivery performance. Selection of the most suitable plant or facility layout design for an organization is one among the most important strategic issues to fulfill all these above-mentioned objectives. Nowadays, many industrial organizations have come to realize the importance of proper selection of the plant or facility layout design to survive in the global competitive market. Selecting the proper layout design from a given set of candidate alternatives is a difficult task, as many potential qualitative and quantitative criteria need to be considered. This paper proposes a Euclidean distance based approach (WEDBA) as a multiple attribute decision making method to deal with the complex plant or facility layout design problems of the industrial environment. Three examples are included to illustrate the approach.

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Modified m‐Polar Fuzzy Set ELECTRE‐I Approach

Jagtap¹,

Karande²,

Patil³

2022

Fuzzy Computing in Data Science

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Flexible manufacturing system selection using a combinatorial mathematics-based decision-making method

Cited by 54 publications

References 37 publications

Flexible manufacturing system selection using preference ranking methods : A comparative study

Flexible manufacturing system selection using preference ranking methods : A comparative study

Weighted Euclidean distance based approach as a multiple attribute decision making method for plant or facility layout design selection

Modified m‐Polar Fuzzy Set ELECTRE‐I Approach

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